Title:
A Guide to Constraining Effective Field Theories with Machine Learning

Abstract: We develop, discuss, and compare several inference techniques to constrain
theory parameters in collider experiments. By harnessing the latent-space
structure of particle physics processes, we extract extra information from the
simulator. This augmented data can be used to train neural networks that
precisely estimate the likelihood ratio. The new methods scale well to many
observables and high-dimensional parameter spaces, do not require any
approximations of the parton shower and detector response, and can be evaluated
in microseconds. Using weak-boson-fusion Higgs production as an example
process, we compare the performance of several techniques. The best results are
found for likelihood ratio estimators trained with extra information about the
score, the gradient of the log likelihood function with respect to the theory
parameters. The score also provides sufficient statistics that contain all the
information needed for inference in the neighborhood of the Standard Model.
These methods enable us to put significantly stronger bounds on effective
dimension-six operators than the traditional approach based on histograms. They
also outperform generic machine learning methods that do not make use of the
particle physics structure, demonstrating their potential to substantially
improve the new physics reach of the LHC legacy results.

Comments:

See also the companion publication "Constraining Effective Field Theories with Machine Learning" at arXiv:1805.00013, a brief introduction presenting the key ideas. The code for these studies is available at this https URL . v2: Added references